Graphics. ggplot2

Step 1: create a blank canvas by specifying data:

ggplot(data = hp2001Q1)

Step 2: specify aesthetic mappings

now we want to map variables to visual aspects: here we map “Land_Value” and “Structure_Cost” to the x- and y-axes.

ggplot(data = hp2001Q1, 
       mapping = aes(x = Land_Value, y = Structure_Cost))

Step 3: add geometric objects:

here we use geom_point() to add a layer with point (dot) elements as the geometric shapes to represent the data.

ggplot(data = hp2001Q1, aes(x = Land_Value, y = Structure_Cost)) + 
  geom_point()

Lines (prediction line)

A plot constructed with ggplot() can have more than one geom. In that case the mappings established in the ggplot() call are plot defaults that can be added to or overridden — this is referred to as aesthetic inheritance. Our plot could use a regression line:

hp2001Q1$pred_SC <- lm(Structure_Cost ~ log(Land_Value), 
                       data = hp2001Q1) |> 
    predict()

Lines (prediction line)

p1 <- ggplot(hp2001Q1, aes(x = log(Land_Value), y = Structure_Cost))
p1 + geom_point(aes(color = Home_Value)) +
     geom_line(aes(y = pred_SC))

Smoothers

Not all geometric objects are simple shapes; the smooth geom includes a line and a ribbon.

p1 +
    geom_point(aes(color = Home_Value)) +
    geom_smooth()

Text (label points)

Each geom accepts a particular set of mappings; for example geom_text() accepts a label mapping.

p1 + 
    geom_text(aes(label = State), size = 3)

Text (label points)

But what if we want to include points and labels? We can use geom_text_repel() to keep labels from overlapping the points and each other.

p1 + 
    geom_point() + 
    geom_text_repel(aes(label = State), size = 3)

Aesthetic mapping VS assignment

1. Variables are mapped to aesthetics inside the aes() function.

p1 +
    geom_point(aes(size = Home_Value))

Aesthetic mapping VS assignment

2. Constants are assigned to aesthetics outside the aes() call

p1 +
    geom_point(size = 2)

Aesthetic mapping VS assignment

This sometimes leads to confusion, as in this example:

p1 +
    geom_point(aes(size = 2),# incorrect! 2 is not a variable
               color="red") # this is fine -- all points red

Mapping variables to other aesthetics

Other aesthetics are mapped in the same way as x and y in the previous example.

p1 +
    geom_point(aes(color = Home_Value, shape = region))

Visualizing distributions. Categorical variable

ggplot(hp2001Q1, aes(x = region)) +
  geom_bar()

Visualizing distributions. Numerical variable

ggplot(hp2001Q1, aes(x = Home_Value)) +
  geom_histogram()

Visualizing distributions. Numerical variable

We can change the binning scheme by passing the binwidth argument to the geom_histogram function

ggplot(hp2001Q1, aes(x = Home_Value)) +
  geom_histogram(binwidth = 4000)

Visualizing distributions. Numerical variable

An alternative visualization for distributions of numerical variables is a density plot. A density plot is a smoothed-out version of a histogram and a practical alternative, particularly for continuous data that comes from an underlying smooth distribution.

ggplot(hp2001Q1, aes(x = Home_Value)) +
  geom_density(linewidth = 1, color="blue")

Visualizing relationships

To visualize the relationship between a numerical and a categorical variable we can use side-by-side box plots. A boxplot is a type of visual shorthand for measures of position (percentiles) that describe a distribution.

ggplot(hp2001Q1, aes(x = region, y = Home_Value)) +
  geom_boxplot()

Visualizing relationships

Alternatively, we can make density plots with geom_density().

ggplot(hp2001Q1, aes(x = Home_Value, color = region)) +
  geom_density(linewidth = 1)

Visualizing relationships

Additionally, we can map species to both color and fill aesthetics and use the alpha aesthetic to add transparency to the filled density curves.

ggplot(hp2001Q1, aes(x = Home_Value, color = region, fill = region)) +
  geom_density(alpha = 0.5)